I would like to apply arbitrarily defined bit mask as virtual aperture and apply it to 4D-STEM data set in an EFFICIENT way.
I did it using the SliceN function and apply the mask pixel-by-pixel, which is very slow for large datasets. How to optimize it to so to run faster?
Image 4DSTEM := GetFrontImage() // dimention [ScanX, ScanY, Dx, Dy]
Image mask: = iradius // just an arbitrary mask (aperture)
Image out // dimention [ScanX, ScanY]
for (number i=0; i<ScanX; i++)
{ for (number j=0; j<ScanY; j++)
{
Diff2D = 4DSTEM.SliceN(4,2,i,j,0,0,2,Dx,1,3,Dy,1)
out.setpixel(i,j, sum(diff2D*mask))
}
}
out.showimage()
for an [100,100,512,512] dataset, that took few minutes to finish. When I have to repeat the operation several times, that is way to slow compare to matrix operation. but I dont know how to make it in an efficient way.
Thanks!
you're hitting the limitations of scripting languages here. Using sliceN is already pretty much the optimum you can get to, unfortunately. Everything else in speed optimization requires parallelized, compiled code. (i.e. you could code C++ code and use the SDK to compile your own plugin.)
However, there is a bit of room for improvement over your example.
First of all, your example above doesn't run :c) But that is quickly fixed.
Point #1:
Try to avoid number type casting. DM script only knows number but internally there is a difference between the proper number types (integer, floating point, signed/unsigned, byte-size). The script languages uses real-4-byte as the default unless told differently explicitly. And some methods will return real-4-byte by default. For this reason, the processing will be fastest, if both data and mask use real-4-byte data as well.
In my testing, the time-difference between running with uint16 data plus uint8 mask and *real4 data plus real4 mask) was significant! Nearly 30% time difference.
Point #2:
Don't copy you sliced image! Use := not = for your Dif2D.
The SliceN command returns an expression directly addressing the required memory. You can use it directly in any other expression (like I do below) or you can assign an image variable to it using := to give it a name.
The speed increase is not huge, but it's one copy-operation less per loop iteration.
Point #3:
You additional knowledge: Now for arbitrary masks there is not much you can do, but most often masks are zero-valued over large stretches and it is possible to define a smaller ROI containing all non-zero points. If this is the case, you can limit your math operations to that region.
i.e. instead of multiplying the whole DP with the same sized mask, just use a smaller mask and use the according sub-section of the DP.
This can actually make a big difference, but it will depend on your mask.
Of course you need to "find" this ROI first. In my script below I'm having a helper method to do that, utilizing the comparatively fast max() command and image rotation as trick for speed-up.
Point #4:
...would be to get rid of the double-for loop and replace it with image-expressions. Unfortunately, DigitalMicrograph does currently (GMS 3.3) not support this for 4D or 5D data.
The script below executed on a [53 x 52 x 512 x 512] STEM DI (of real-4 byte data) gave me the following timings:
Original: 12.80910 sec
Test 1 : 10.77700 sec
Test 2 : 1.83017 sec
// Helper class for timing
class CTimer{
number s
string n
~CTimer(object self){result("\n"+n+": "+ (GetHighResTickCount()-s)/GetHighResTicksPerSecond()+" sec");}
object Start(object self, string n_) { n=n_; s=GetHighResTickCount(); return self;}
}
// Helper method to find best non-zero containing ROI
void GetNonZeroArea( image src, number &t, number &l, number &b, number &r )
{
image work = !!src // Make a binary image which is 0 only where src==0
number d
max(work,d,t) // get "first" non-zero pixel coordinate, this is y = dist from TOP
rotateRight(work) // rotate image right
max(work,d,l) // get "first" non-zero pixel coordinate, this is y = dist from LEFT
rotateRight(work) // rotate image right
max(work,d,b) // get "first" non-zero pixel coordinate, this is y = dist from BOTTOM
b = work.ImageGetDimensionSize(1) - b // Opposite side!
rotateRight(work) // rotate image right
max(work,d,r) // get "first" non-zero pixel coordinate
r = work.ImageGetDimensionSize(1) - r // Opposite side!
}
// The original proposed script (plus fixes to make it actually run)
image Original(image STEM4D, image mask)
{
Number ScanX = STEM4D.ImageGetDimensionSize(0)
Number ScanY = STEM4D.ImageGetDimensionSize(1)
Number Dx = STEM4D.ImageGetDimensionSize(2)
Number Dy = STEM4D.ImageGetDimensionSize(3)
Image out := RealImage("Test1",4,ScanX,ScanY)
for (number i=0; i<ScanX; i++)
{ for (number j=0; j<ScanY; j++)
{
image Diff2D = STEM4D.SliceN(4,2,i,j,0,0,2,Dx,1,3,Dy,1)
out.setpixel(i,j, sum(Diff2D*mask))
}
}
return out
}
// Remove copying the slice, just reference it
image Test1(image STEM4D, image mask)
{
Number ScanX = STEM4D.ImageGetDimensionSize(0)
Number ScanY = STEM4D.ImageGetDimensionSize(1)
Number Dx = STEM4D.ImageGetDimensionSize(2)
Number Dy = STEM4D.ImageGetDimensionSize(3)
Image out := RealImage("Test1",4,ScanX,ScanY)
for (number i=0; i<ScanX; i++)
{ for (number j=0; j<ScanY; j++)
{
image Diff2D := STEM4D.SliceN(4,2,i,j,0,0,2,Dx,1,3,Dy,1)
out.setpixel(i,j, sum(Diff2D*mask))
}
}
return out
}
// Limit mask size to what is needed!
image Test2(image STEM4D, image mask )
{
Number ScanX = STEM4D.ImageGetDimensionSize(0)
Number ScanY = STEM4D.ImageGetDimensionSize(1)
Number Dx = STEM4D.ImageGetDimensionSize(2)
Number Dy = STEM4D.ImageGetDimensionSize(3)
Image out := RealImage("Test1",4,ScanX,ScanY)
Number t,l,b,r
GetNonZeroArea(mask,t,l,b,r)
Number w = r - l
Number h = b - t
image subMask := mask.slice2(l,t,0, 0,w,1, 1,h,1 )
for (number i=0; i<ScanX; i++)
for (number j=0; j<ScanY; j++)
out.setpixel(i,j, sum(STEM4D.SliceN(4,2,i,j,l,t,2,w,1,3,h,1)*subMask))
return out
}
Image src := GetFrontImage() // dimention [ScanX, ScanY, Dx, Dy]
Number ScanX = src.ImageGetDimensionSize(0)
Number ScanY = src.ImageGetDimensionSize(1)
Number Dx = src.ImageGetDimensionSize(2)
Number Dy = src.ImageGetDimensionSize(3)
Number r = 50 // mask radius
Image maskImg := RealImage("Mask",4,Dx,Dy)
maskImg = iradius < r ? 1 : 0 // just an aperture mask
image resultImg
{
object timer = Alloc(CTimer).Start("Original")
resultImg := Original(src,maskImg)
}
resultImg.SetName("Oringal")
resultImg.ShowImage()
{
object timer = Alloc(CTimer).Start("Test 1")
Test1(src,maskImg).ShowImage()
}
resultImg.SetName("Test 1")
resultImg.ShowImage()
{
object timer = Alloc(CTimer).Start("Test 2")
Test2(src,maskImg).ShowImage()
}
resultImg.SetName("Test 2")
resultImg.ShowImage()
Compiled code comparison:
Now, it should be added that the above script still is rather slow. Because it is iterating and using script language. The fully compiled c++ code of DigitalMicrograph is much faster. So if you have the licensed packages giving you the SI menu, then you want to use the SI/Map/Signal command. This is near-instantaneous for the example STEM DI I've mentioned above. My other answer shows how one could utilize this functionality by script.
As mentioned in my other answer, a real speed-win comes when compiled, parallelized code is used. DigitalMicrograph does this, after all, in the available SI "signal" map functionality. This feature is not available in the free version, but if you have Spectrum-Imaging acquisition, you most likely have the appropriated license as well.
The answer below utilizes this functionality by accessing the UI with the command ChooseMenuItem() and applying a few more tricks. The script is a bit lengthy, but its parts also show some other nice tricks worthwhile knowing:
TestSignalIntegrationInSI is the main script demoing how things can work.
CreatePickerByScript shows how one can create picker-spectra on SIs. This is used to open a 'Picker Diffraction Pattern' image from the STEM DI.
AddTestMasksToDP_ROIs programmatically adds ROIs to the diffraction pattern to be used as mask
AddTestMasksToDP_Threshold programmatically adds an intensity-threshold mask to be used as mask.
AddTestMasksToDP_DPMasks programmatically adds the various types of diffraction-masks to be used as mask
GetIntegratedSignalViaSIMenu is the central step of the script. With a picker-DP and required 'masks' on it front-most, the menu command is called to perform the signal-extraction (as fast as possible.) Then the displayed result-image is returned.
GetNewestImage is just a utility method showing how on can access the latest memory-created image.
Here is the script:
image GetNewestImage()
{
// New images get the next higher imageID.
// This can be used to identify the "latest" created image.
if ( 0 == CountImages() ) Throw( "No image in memory!" )
// We create a temp. image to get the uppder limit
number lastID = RealImage("Dummy",4,1).ImageGetID()
// Then we search for the next lower existing one
image lastImg
for( number ID = lastID - 1; ID>0; ID-- )
{
lastImg := FindImageByID(ID)
if ( lastImg.ImageIsValid() ) break
}
return lastImg
}
image CreatePickerByScript( image SI, number t, number l, number b, number r )
{
if ( SI.ImageGetNumDimensions()<3 ) Throw( "Sorry, LineScans are not supprorted here." )
// Adding a non-volatile ROI of specific RoiNAME acts as if using
// the picker-tool. The ID string must be unique!
ROI pickerROI = NewROI()
pickerROI.RoiSetVolatile( 0 )
string uniqueID = GetDate(0)+"#"+GetTime(1)+";"+round(random()*1000)
pickerROI.RoiSetName( "SICursor(##"+uniqueID+"##)" )
SI.ImageGetImageDisplay(0).ImageDisplayAddROI( pickerROI )
// This creates the picker image.
// So the child is now the "newest" image in memory
image child := GetNewestImage()
return child
}
void AddTestMasksToDP_ROIs( image DP )
{
// Add ROIs to the DP which are your masks (any numebr and type of ROI works)
imageDisplay DPdisp = DP.ImageGetImageDisplay(0)
number dpX = DP.ImageGetDimensionSize(0)
number dpY = DP.ImageGetDimensionSize(1)
// Only simple RECT ROIs are supported
ROI maskRoi1 = NewROI()
maskRoi1.ROISetRectangle( dpY*0.1, dpX*0.1, dpY*0.8, dpX*0.3 )
DPdisp.ImageDisplayAddROI(maskRoi1)
// Arbitrary multi-vertex (use for ovals etc.)
ROI maskRoi2 = NewROI()
maskRoi2.ROISetRectangle( dpY*0.7, dpX*0.1, dpY*0.9, dpX*0.9 )
DPdisp.ImageDisplayAddROI(maskRoi2)
}
void AddTestMasksToDP_Threshold( image DP )
{
// Add intensity treshhold mask (highest 95% intensity range)
imageDisplay DPdisp = DP.ImageGetImageDisplay(0)
DPdisp.RasterImageDisplaySetThresholdOn( 1 )
number low = max(DP) * 0.05
number high = max(DP)
DPdisp.RasterImageDisplaySetThresholdLimits( low, high )
}
void AddTestMasksToDP_DPMasks( image DP )
{
// Add Diffraction masks to the DP
imageDisplay DPdisp = DP.ImageGetImageDisplay(0)
// Spot masks (always symmetric pair)
Component spotMask = NewComponent(8,0,0,0,0) // 8 = Spotmask
spotMask.ComponentSetControlPoint(4, 0, 0,0) // 4 = TopLeft of one spot [Size only]
spotMask.ComponentSetControlPoint(7,10,10,0) // 7 = BottomRight of one spot [Size only]
spotMask.ComponentSetControlPoint(8,150,0,0) // 8 = Spot position [center]
DPdisp.ComponentAddChildAtEnd(spotMask)
// Bandpass mask (Only circles are correctly supported)
Component bandpassMask = NewComponent(15,0,0,0,0) // 15 = Bandpass (ring)
number r1 = 100
number r2 = 120
bandpassMask.ComponentSetControlPoint(7,r1,r1,0) // 7 = BottomRight of one ring [Size only]
bandpassMask.ComponentSetControlPoint(14,r2,r2,0) // 14 = BottomRight of one ring [Size only]
DPdisp.ComponentAddChildAtEnd(bandpassMask)
// Wege mask (symmetric)
Component wedgeMask = NewComponent(19,0,0,0,0) // 19 = wedgemask (ringsegment)
wedgeMask.ComponentSetControlPoint(9,10,20,0) // 9 = One wedge vector
wedgeMask.ComponentSetControlPoint(10,-20,40,0) // 10 = Other wedge vector
DPdisp.ComponentAddChildAtEnd(wedgeMask)
// Array mask (symmetric)
Component arrayMask = NewComponent(9,0,0,0,0) // 9 = arrayMask (ringsegment)
arrayMask.ComponentSetControlPoint(9,-70,-60,0) // 9 = One array vector
arrayMask.ComponentSetControlPoint(10,99,-99,0) // 10 = Other array vector
arrayMask.ComponentSetControlPoint(4, 0, 0,0) // 4 = TopLeft of one spot [Size only]
arrayMask.ComponentSetControlPoint(7,20,20,0) // 7 = BottomRight of one spot [Size only]
DPdisp.ComponentAddChildAtEnd(arrayMask)
}
image GetIntegratedSignalViaSIMenu( image pickerChild )
{
// Call the Menu to do the work
// The picker-spectrum or DP needs to be front-most
pickerChild.SelectImage()
ChooseMenuItem("SI","Map","Signal")
// The created signal map is NOT the newest image
// (some internal iamges are created for the mask)
// but it is the front-most displayed one.
image signalMap := GetFrontImage()
return signalMap
}
image GetMaskFromSignalMap( image signalMap, number DPx, number DPy )
{
// The actual mask is stored in the tags
string tagPath = "Processing:[0]:Parameters:Mask"
tagGroup tg = signalMap.ImageGetTagGroup()
if ( !tg.TagGroupDoesTagExist(tagPath) )
Throw( "Sorry, no mask tag found." )
image mask := RealImage("Mask",4,DPx, DPy )
if ( !tg.TagGroupGetTagAsArray(tagPath,mask) )
Throw( "Sorry, could not retrieve mask. Maybe wrong size?" )
return mask
}
void TestSignalIntegrationInSI()
{
image STEMDI := GetFrontImage()
image DP := STEMDI.CreatePickerByScript(0,0,1,1)
if ( TwoButtonDialog( "Add ROIs as mask?", "Yes", "No" ) )
AddTestMasksToDP_ROIs( DP )
else if ( TwoButtonDialog( "Add intensity treshold as mask?", "Yes", "No" ) )
AddTestMasksToDP_Threshold( DP )
else if ( TwoButtonDialog( "Add diffraction masks as mask?", "Yes", "No" ) )
AddTestMasksToDP_DPMasks( DP )
image signalMap := GetIntegratedSignalViaSIMenu( DP )
number dpX = DP.ImageGetDimensionSize(0)
number dpY = DP.ImageGetDimensionSize(1)
// We may want to close the DP again. No longer needed
//DP.DeleteImage()
// Verification: Get Mask image form SignalMap
image usedMask := GetMaskFromSignalMap( signalMap, dpX, dpY )
usedMask.SetName( "This mask was used." )
usedMask.ShowImage()
}
TestSignalIntegrationInSI()
The solution below utilizes the intrinsic expression loops by performing in-place multiplication and then projection.
Disappointingly, it turns out the solution is actually a bit slower then the for-loop with the SliceN command.
For the same test-data of size [53 x 52 x 512 x 512] the resulting timing is:
Data copy: 1.28073 sec
Inplace multiply: 30.1978 sec
Project 1/2: 1.1208 sec
Project 2/2: 0.0019557 sec
InPlace multiplication with projections (total): 32.9045 sec
InPlace multiplication with projections (total): 34.9853 sec
// Helper class for timing
class CTimer{
number s
string n
~CTimer(object self){result("\n"+n+": "+ (GetHighResTickCount()-s)/GetHighResTicksPerSecond()+" sec");}
object Start(object self, string n_) { n=n_; s=GetHighResTickCount(); return self;}
}
image MaskMultipliedSum( image STEM4D, image MASK2D, number copyFirst )
{
// Boring feasability checks...
if ( 4 != STEM4D.ImageGetNumDimensions() )
Throw( "Input data is not 4D." )
if ( 2 != MASK2D.ImageGetNumDimensions() )
Throw( "Input mask is not 2D." )
Number ScanX = STEM4D.ImageGetDimensionSize(0)
Number ScanY = STEM4D.ImageGetDimensionSize(1)
Number Dx = STEM4D.ImageGetDimensionSize(2)
Number Dy = STEM4D.ImageGetDimensionSize(3)
if ( Dx != MASK2D.ImageGetDimensionSize(0) )
Throw ("X dimension of mask does not match input data." )
if ( Dy != MASK2D.ImageGetDimensionSize(1) )
Throw ("Y dimension of mask does not match input data." )
// Do the maths!
image workCopy4D
if ( copyFirst )
{
object timer = Alloc(CTimer).Start("Data copy")
workCopy4D = STEM4D
}
else
workCopy4D := STEM4D
{
object timer = Alloc(CTimer).Start("Inplace multiply")
workCopy4D *= MASK2D[idimindex(2),idimindex(3)]
}
// Now we want to "sum up" over Dx and Dy
image p1,p2
{
object timer = Alloc(CTimer).Start("Project 1/2")
p1 := project( workCopy4D, 3 )
}
{
object timer = Alloc(CTimer).Start("Project 2/2")
p2 := project( p1, 2 )
}
return p2
}
image stack4D, mask2D
If ( GetTwoLabeledImagesWithPrompt("Please select 4D data and 2D mask", "Select input", "4D data", stack4D, "2D mask", mask2D ) )
{
number doCopy = TwoButtonDialog("Create workcopy?","Yes (takes time)","No (overwrites input data!)")
object timer = Alloc(CTimer).Start("InPlace multiplication with projections (total)")
MaskMultipliedSum(stack4D,mask2D,doCopy).ShowImage()
}
I have a 6-DOF robot arm model:
robot arm structure
I want to calculate forward kinematics, so I uses the D-H matrix. the D-H parameters are:
static const std::vector<float> theta = {
0,0,90.0f,0,-90.0f,0};
// d
static const std::vector<float> d = {
380.948f,0,0,-560.18f,0,0};
// a
static const std::vector<float> a = {
-220.0f,522.331f,80.0f,0,0,94.77f};
// alpha
static const std::vector<float> alpha = {
90.0f,0,90.0f,-90.0f,-90.0f,0};
and the calculation :
glm::mat4 Robothand::armForKinematics() noexcept
{
glm::mat4 pose(1.0f);
float cos_theta, sin_theta, cos_alpha, sin_alpha;
for (auto i = 0; i < 6;i++)
{
cos_theta = cosf(glm::radians(theta[i]));
sin_theta = sinf(glm::radians(theta[i]));
cos_alpha = cosf(glm::radians(alpha[i]));
sin_alpha = sinf(glm::radians(alpha[i]));
glm::mat4 Ai = {
cos_theta, -sin_theta * cos_alpha,sin_theta * sin_alpha, a[i] * cos_theta,
sin_theta, cos_theta * cos_alpha, -cos_theta * sin_alpha,a[i] * sin_theta,
0, sin_alpha, cos_alpha, d[i],
0, 0, 0, 1 };
pose = pose * Ai;
}
return pose;
}
the problem I have is that, I can't get the correct result, for example, I want to calculate the transformation matrix from first joint to the 4th joint, I will change the for loop i < 3,then I can get the pose matrix, and I can the origin coordinate in 4th coordinate system by pose * (0,0,0,1).but the result (380.948,382.331,0) seems not correct because it should be move along x-axis not y-axis. I have read many books and materials about D-H matrix, but I can't figure out what's wrong with it.
I have figured it out by myself, the real problem behind is glm::mat, glm::mat is col-type which means columns will be initialized before rows,I changed the code and get the correct result:
for (int i = 0; i < joint_num; ++i)
{
pose = glm::rotate(pose, glm::radians(degrees[i]), glm::vec3(0, 0, 1));
pose = glm::translate(pose,glm::vec3(0,0,d[i]));
pose = glm::translate(pose, glm::vec3(a[i], 0, 0));
pose = glm::rotate(pose,glm::radians(alpha[i]),glm::vec3(1,0,0));
}
then I can get the position by:
auto pos = pose * glm::vec4(x,y,z,1);
Is there a script command that I can specify a particular slice in a LinePlotImageDisplay and do the Align Slice Horizontally by Calibration (or Uncalibrated (channels)) action?
The following scipt is a complete implantation based on example codes provided by BmyGuest. It will align all slices in a LinePlotImageDisplay horizontally either by calibration or by channel (i.e. un-calibrated).
class SliceAlignment : object {
number true, false; // boolean
image imgLPID;
imageDisplay LPID; // line plot image display
number CalculateImageToGroupTransformFactors( object self, image slice_src, image slice_ref, number &relOff, number &relScale ) {
number origin_ref, scale_ref, origin_src, scale_src;
string unit_ref, unit_src;
number calFMT = 0; // origin is expressed in calibrated unit
//
slice_src.ImageGetDimensionCalibration( 0, origin_src, scale_src, unit_src, calFMT );
slice_ref.ImageGetDimensionCalibration( 0, origin_ref, scale_ref, unit_ref, calFMT );
//
relScale = scale_src / scale_ref;
relOff = (origin_src - origin_ref) / scale_ref ;
// check if both images are calibrated in same unit
if( unit_src != unit_ref ) return false
return true;
};
void AlignNthSliceHorizontallyByChannel( object self, number slice_idx ) {
// get current reference slice index
number refSlice_idx = LPID.LinePlotImageDisplayGetSlice();
// get slice ID's (as objects)
object slice_ref = LPID.ImageDisplayGetSliceIDByIndex(refSlice_idx);
object slice_src = LPID.ImageDisplayGetSliceIDByIndex(slice_idx);
number int_offset = 0, int_scale = 1.0; // vertical (intensity) offset and scaling factors
number pos_offset = 0, pos_scale = 1.0; // horizontal (position) offset and scaling factors
LPID.LinePlotImageDisplaySetImageToGroupTransform( slice_src, slice_ref, int_offset, int_scale, pos_offset, pos_scale );
};
void AlignNthSliceHorizontallyByCalibration( object self, number slice_idx ) {
// get current reference slice index
number refSlice_idx = LPID.LinePlotImageDisplayGetSlice();
// get slice ID's (as objects)
object slice_ref = LPID.ImageDisplayGetSliceIDByIndex(refSlice_idx);
object slice_src = LPID.ImageDisplayGetSliceIDByIndex(slice_idx);
number int_offset = 0, int_scale = 1.0; // vertical (intensity) offset and scaling factors
number pos_offset, pos_scale; // horizontal (position) offset and scaling factors
number unit_check = self.CalculateImageToGroupTransformFactors( imgLPID{slice_idx}, imgLPID{refSlice_idx}, pos_offset, pos_scale );
if( unit_check == false ) {
string prompt = "slice #" + slice_idx + " [" + LPID.ImageDisplayGetSliceLabelById( LPID.ImageDisplayGetSliceIDByIndex(slice_idx) ) + "] is calibrated in different unit!";
if( !ContinueCancelDialog( prompt ) ) return
};
LPID.LinePlotImageDisplaySetImageToGroupTransform( slice_src, slice_ref, int_offset, int_scale, pos_offset, pos_scale );
return;
};
void AlignAllSlicesHorizontallyByChannel( object self ) {
number nSlices = LPID.LinePlotImageDisplayCountSlices();
for( number idx = 0; idx < nSlices; idx++ ) self.AlignNthSliceHorizontallyByChannel( idx );
return;
};
void AlignAllSlicesHorizontallyByCalibration( object self ) {
number nSlices = LPID.LinePlotImageDisplayCountSlices();
for( number idx = 0; idx < nSlices; idx++ ) self.AlignNthSliceHorizontallyByCalibration( idx );
return;
};
object init( object self, image img ) {
// check if the image display is correct type
imgLPID := img;
LPID = imgLPID.ImageGetImageDisplay(0);
if( LPID.ImageDisplayGetDisplayType() != 3 ) throw( "Please choose a valid line plot display" );
return self;
};
SliceAlignment( object self ) {
true = 1; false = 0;
result( "SliceAlignment [obj ID:" + self.ScriptObjectGetID().hex() + "] constructured\n" );
};
~SliceAlignment( object self ) {
result( "SliceAlignment [obj ID:" + self.ScriptObjectGetID().hex() + "] destructured\n\n" );
}; };
{; object objAlign = alloc(SliceAlignment);
objAlign.init( GetFrontImage() );
if( OptionDown() ) objAlign.AlignAllSlicesHorizontallyByChannel();
else objAlign.AlignAllSlicesHorizontallyByCalibration(); };
No, there is no single 'convenience' command to achieve this alignment. You will have to create the according function yourself from reading a slices calibration and setting its display-coordinate system. You might find the following (old) tutorial PDF on the FELMI homepage might be useful:
SlicesInLinePlotDisplay.pdf
The following example script might also be useful. It shows how one slice is aligned relative to another slice. (Just on the X-axis)
// All Slices in a LinePlot are grouped into a single 'group'
// Slices can be moved relative to each other by specifying their image-to-group transform,
// and the whole image (i.e. the group) can be moved with respect to the display using the group-to-display transform.
// To set the image-to-group transform of the slice specified by 'slice_id', with respect to the slice specified by 'ref_id'
// use the command:
// LinePlotImageDisplaySetImageToGroupTransform( LinePlotImageDisplay lpid, ScriptObject slice_id, ScriptObject ref_id, double off_val, double scale_val, double off_dim_0, double scale_dim_0 )
/*********************************************************/
// Create 2 LinePlots and add them into one display
// (Initially they are aligned by their calibrations)
number sc1 = 1
number of1 = -50
number sc2 = 2
number of2 = -20
image sl1 := realImage("S1",4,300)
image sl2 := realImage("S2",4,300)
sl1 = (iwidth-icol)/iwidth
sl2 = (iwidth-icol)/iwidth
sl1[0,50,1,60] = 1
sl1[0,250,1,260] = 1
sl2[0,10,1,15] = 1
sl2[0,110,1,115] = 1
// Adding Calibrations
sl1.ImageSetDimensionCalibration(0,of1,sc1,"CH",0)
sl2.ImageSetDimensionCalibration(0,of2,sc2,"CH",0)
sl1.DisplayAt(20,30)
sl2.DisplayAt(750,30)
OKDialog( "Put into one Display" )
imageDisplay disp = sl1.ImageGetImageDisplay(0)
disp.ImageDisplayAddImage( sl2, "S2") // When added like this, the slices are automatically aligned by their respective calibration!
disp.LinePlotImageDisplaySetDoAutoSurvey( 0, 0 )
object ref_id = disp.ImageDisplayGetSliceIDByIndex(0) // Slice 0
object slice_id = disp.ImageDisplayGetSliceIDByIndex(1) // Slice 1
OKDialog("Now align by channels (i.e. undo any relative sclice alignment)")
// Simply set the relative "shifts" and "scales" to 0 and 1.
disp.LinePlotImageDisplaySetImageToGroupTransform( slice_id, ref_id, 0, 1, 0, 1 )
OKDialog("Now align by chalibration ")
number relScale = sc2/sc1
number relOff = of2-of1
disp.LinePlotImageDisplaySetImageToGroupTransform( slice_id, ref_id, 0, 1, relOff, relScale )
I want that my phaser world scales at the point where my mouse pointer is (or between 2 fingers/pinch). Per default, phaser scales/zooms around the top left corner.
Here is an example (but not for phaser) that shows what exactly I want to achive: https://cloudup.com/blog/how-we-made-zoom-on-mobile-using-css3-and-js
To scale a group of items based on a the clicked point.
Capture the start position of the group (x,y) and the mouse click point:
startPosX = this.position.x;
startPosY = this.position.y;
clickX = pointer.x;
clickY = pointer.y;
Perform the scale of the group:
var tweenScale = game.add.tween(this.scale).to( { x: 2, y: 2 }, 10,
Phaser.Easing.Linear.None, true, 0, 0, false);
On scale complete, tween back to mouse click location:
tweenScale.onComplete.add(function() {
var u = ((1-this.scale.x) * clickX) + startPosX;
var v = ((1-this.scale.y) * clickY) + startPosY;
game.add.tween(this.position)
.to({ x: this.position.x + u, y: this.position.y + v}, 10,
Phaser.Easing.Linear.None, true, 0, 0, false );
}, this);
I added startPosX to get u (and v respectively) to account for the location of the original starting point of the group or else it would be off, by that amount. I'm sure there are different ways to approach the tween chaining, but the calculations for u and v are what's important.
Used this answer to derive the basic calculation: https://stackoverflow.com/a/39344716/1956540
See these examples of working implementations:
http://test.xapient.net/phaser/springtest/indexzoom.html (a and o)